1. Field of Art
The invention generally relates to computer vision, and more specifically, to articulate object tracking.
2. Description of the Related Art
Developing an accurate, efficient and robust visual object tracker is a challenging problem in the field of computer vision. The task becomes even more difficult when the target object undergoes significant and rapid variation in shape as well as appearance. Such shape variation is common when tracking an articulated object (such as a human) due to the frequent and varied self-occlusions that may occur as the object moves.
One conventional approach to object tracking uses intensity histograms to represent the appearance of the object. Examples of such conventional approaches are described in S. Birchard. “Elliptical Head Tracking Using Intensity Gradients and Color Histograms.” In Proc. IEEE Conf. on Comp. Vision and Patt. Recog., pages 232-237, 1998; D. Comaniciu, V. Ramesh, and P. Meer. “Real-time Tracking of Non-Rigid Objects Using Mean Shift.” In Proc. IEEE Conf. on Comp. Vision and Patt. Recog., pages 2142-2147, 2000; P. Viola and M. Jones. “Rapid Object Detection Using a Boosted Cascade of Simple Features. In Proc. IEEE Conf on Comp. Vision and Patt. Recog., volume 1, pages 511-518, 2001; F. Porkli. “Integral Histogram: A Fast Way to Extract Histograms in Cartesian Spaces. In Proc. IEEE Conf. on Comp. Vision and Patt. Recog. Volume 1, pages 829-836, 2005; and A. Adam, E. Rivlin, and I. Shimshoni. “Robust Fragments-Based Tracking Using Integral Histogram.” In Proc. IEEE Conf. on Comp. Vision and Patt. Recog., pages 2142-2147, 2006, the contents of which are all incorporated by reference herein in their entirety.
These conventional approaches are typically applied to tracking objects having a regular shape (such as a rectangle). However, the conventional techniques are not sufficiently robust and efficient when applied to objects that have highly irregular shapes. Furthermore, such techniques do not work well when the object undergoes rapid and/or large motions that cause the shape to vary widely between image frames.
To handle shape variation in the context of histogram based tracking, one conventional approach is to use a (circular or elliptical) kernel to define a region around the target from which a weighted histogram can be computed. Instead of scanning the image, differential algorithms are used to iteratively converge to the target object. Examples of such approaches are described in Z. Fan, M. Yang, Y. Wu, and G. Hua. “Efficient Optimal Kernel Placement for Reliable Visual Tracking.” In Proc. IEEE Conf. on Comp. Vision and Patt. Recog. 2007; and V. Parameswaran, V. Ramesh, and I. Zoghlami. “Tunable Kernels for Tracking.” In Proc. IEEE Conf. on Comp. Vision and Patt. Recog. Volume 2, pages 2179-2186, 2006, the contents of which are incorporated by reference herein in their entirety.
The kernel used in the above approaches effectively reduces the more difficult problem of efficiently computing the intensity histogram from an irregular shape to that of a simpler one for estimating histogram from a regular shape. However, these differential approaches are still insufficient for tracking sequences with rapid and large motions, such as, for example, human motion.
Another conventional approach to tracking objects having irregular shapes is to enclose the target with a regular shape (e.g., a rectangular window) and compute the histogram from the enclosed region. However, such histograms inevitably include background pixels when the foreground shape cannot be closely approximated by the regular-shaped window. Consequently, the resulting histogram is corrupted by background pixels, and the tracking result degrades accordingly.
The histogram-based tracking methods are largely unsuccessful due to the lack of spatial information. For targets undergoing significant shape variations, the spatial component of the appearance is very prominent and the plain intensity histogram becomes visibly inadequate with unstable tracking results. While some approaches have attempted to incorporate spatial information into the histograms, these approaches require substantial increase of computation loads, thereby making these algorithms applicable to only local search and infeasible for global scans of images. Consequently, such algorithms are not able to track objects undergoing rapid motions.
In view of the present state of the art, there is a need for an object tracking system and method that efficiently and robustly tracks an articulated object in the presence of rapid motion and shape variation.